The current framework for constructing intelligent tutoring systems is to use psychological/pedagogical theories of learning and encode this knowledge into the tutor. However, this approach is both expensive and not sufficiently flexible to support reasoning that some system designers would like intelligent tutors to do. Therefore, we propose using machine learning to automatically derive models of student performance.

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